A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed
Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/3958127 |
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| _version_ | 1832552469151875072 |
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| author | Eui-Jin Kim Ho-Chul Park Seung-Young Kho Dong-Kyu Kim |
| author_facet | Eui-Jin Kim Ho-Chul Park Seung-Young Kho Dong-Kyu Kim |
| author_sort | Eui-Jin Kim |
| collection | DOAJ |
| description | Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented. |
| format | Article |
| id | doaj-art-f28537639b8046f6be86a2d76fdda643 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-f28537639b8046f6be86a2d76fdda6432025-02-03T05:58:36ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/39581273958127A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel SpeedEui-Jin Kim0Ho-Chul Park1Seung-Young Kho2Dong-Kyu Kim3Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of KoreaDepartment of Transportation Engineering, Myongji University, Yongin 17058, Republic of KoreaDepartment of Civil and Environmental Engineering and Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Republic of KoreaDepartment of Civil and Environmental Engineering and Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Republic of KoreaPredicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.http://dx.doi.org/10.1155/2019/3958127 |
| spellingShingle | Eui-Jin Kim Ho-Chul Park Seung-Young Kho Dong-Kyu Kim A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed Journal of Advanced Transportation |
| title | A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed |
| title_full | A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed |
| title_fullStr | A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed |
| title_full_unstemmed | A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed |
| title_short | A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed |
| title_sort | hybrid approach based on variational mode decomposition for analyzing and predicting urban travel speed |
| url | http://dx.doi.org/10.1155/2019/3958127 |
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